Comparison of airborne surveying techniques for mapping submerged objects in shallow water

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In this study, bathymetric lidar, high resolution aerial imagery, and hyperspatial resolution imagery collected from a small unmanned aircraft system (UAS) were examined in order to delineate submerged objects in shallow coastal water. A region surrounding Shamrock Island in Corpus Christi Bay along the Texas Gulf Coast was chosen for this study. This area is significant because of the existence of submerged structures including oil pipelines, which may influence the marine environment and navigation in shallow water. Therefore, mapping submerged structures is the first step of any further study in this area in terms of environmental litter and navigation hazards.
Different methods were compared to each other in these categories in terms of efficiency and accuracy to map the bathymetric surface and detect submerged structures. First, three different interpolation methods including 2D Delaunay triangulated irregular network (TIN), inverse distance weight (IDW), and multilevel B spline were used to create digital elevation models (DEMs) using airborne lidar data to investigate their use on submerged pipeline detection. Then three different algorithms including Sobel, Prewitt, and Canny were examined in edge detection image processing to illustrate the potential pipelines using aerial imagery. To improve visibility, glint correction methods were implemented and compared to non-glint corrected imagery for pipeline delineation. Finally, a small UAS equipped with a digital camera was flown to evaluate structure from motion (SfM) photogrammetry for bathymetric mapping in the shallow bay. Methods examined included glint corrected imagery and single bands vs. original multiband imagery. The goal was to determine the effectiveness of image pre-conditioning methods for improving UAS-SfM mapping of submerged bottom and structures in shallow water.
Results showed that B-spline interpolation method was the best fit compared to other methods for deriving bathymetric DEMs from the airborne lidar data. In edge detection image processing, Canny method performed better between all three methods in detecting the pipelines in the aerial imagery. In the last part, using glint removal methods and green single band imagery as inputs into the UAS-SfM photogrammetry workflow increased the quality of the produced point cloud over shallow water in terms of point density and depth estimation respectively.
In conclusion, bathymetric lidar data in fusion with aerial imagery improved the pipeline delineation. Due to inherent limitations in current bathymetric lidar system resolvance power, it is recommended that future surveys targeted for this objective plan as best as possible for ideal water conditions in terms of visibility, employ more scan overlap. Sun glint correction improved the quality of the imagery in terms of penetrating through the water column. Avoiding sun glint by choosing appropriate place and time for data collection is the best way to deal with sun glint. In the UAS-SfM part, using a polarized filter on RGB cameras is recommended to assess the sun glint effect in the result.